In recent years, various techniques for detecting an object such as a human face from an image have been proposed. For example, the following object detection technique has been proposed. A template created from a reference image is overlaid on an object search target image, and is scanned. At respective overlaying positions, distances (or similar values or similarities) between the object search target image and template (or between feature amounts of the object search target image and those of the template) are computed. Subsequently, an overlaying position where a minimum distance is obtained (or an overlaying position where a maximum similarity between the object search target image and template is obtained) is output as an object detection position.
In order to accurately search for an object, for example, a template is shifted pixel by pixel with respect to an object search target image, and similarities between the object search target image and template are computed in correspondence with respective shift positions.
Also, an object size is often unknown. For this reason, a plurality of reduced-scale images having different sizes are prepared to assume an object, and an object search is conducted for the plurality of assumed reduced-scale images using a template.
However, with the above object search method, the computation load of the object search is heavy, resulting in high computation cost. That is, the object search load is heavy. Hence, an efficiency enhancement technique for the object search is demanded.